Hu Xuechun, Xia Yu, Lendek Zsófia, Cao Jinde, Precup Radu-Emil
School of Mechanical Engineering, Guizhou University, Guiyang 550025, China.
State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Neural Netw. 2025 Oct;190:107627. doi: 10.1016/j.neunet.2025.107627. Epub 2025 May 27.
In order to meet the performance requirements of permanent magnet synchronous motor (PMSM) systems with time-varying model parameters and input constraints under step load, this paper proposes a dynamic prescribed performance fuzzy-neural backstepping control approach. Firstly, a novel finite-time asymmetric dynamic prescribed performance function (FADPPF) is proposed to tackle the issues of exceeding predefined error, control singularity, and system instability that arise in the traditional prescribed performance function under load changes. To address model accuracy degradation and control quality deterioration caused by nonlinear time-varying parameters and input constraints in the PMSM system, a backstepping controller is designed by combining the speed function (SF), fuzzy neural network (FNN), and the proposed FADPPF. The FNN approximates nonlinear uncertain functions in the system model; the SF, as an error amplification mechanism, works together with FADPPF to ensure the transient and steady-state performance of the system. The stability of the devised control strategy is proved using Lyapunov analysis. Finally, simulation results demonstrate the dynamic self-adjusting ability and effectiveness of FADPPF under step load. In addition, the feasibility and superiority of the proposed control scheme are validated.
为了满足具有时变模型参数和阶跃负载下输入约束的永磁同步电机（PMSM）系统的性能要求，本文提出了一种动态规定性能模糊神经反步控制方法。首先，提出了一种新颖的有限时间非对称动态规定性能函数（FADPPF），以解决传统规定性能函数在负载变化下出现的超出预定义误差、控制奇异性和系统不稳定等问题。为了解决永磁同步电机系统中非线性时变参数和输入约束导致的模型精度下降和控制质量恶化问题，通过结合速度函数（SF）、模糊神经网络（FNN）和所提出的FADPPF设计了一种反步控制器。FNN逼近系统模型中的非线性不确定函数；SF作为一种误差放大机制，与FADPPF协同工作，以确保系统的瞬态和稳态性能。利用李雅普诺夫分析证明了所设计控制策略的稳定性。最后，仿真结果展示了FADPPF在阶跃负载下的动态自调整能力和有效性。此外，验证了所提出控制方案的可行性和优越性。
